A physics-informed generative model can extract deep structural knowledge from raw experimental data by combining minimal domain constraints with modern deep learning—showing that fundamental physics principles emerge naturally from data.
Researchers trained a single transformer-based generative model on real particle collision data to learn fundamental physics structures without explicit instruction.